Authors:Mohammad Reza Mohebbi1;2;Elahe Kafash3 andMario Döller1
Affiliations:1Josef Ressel Center Vision2Move, University of Applied Sciences Kufstein Tirol, Kufstein, Austria;2Department of Computer Science, University of Passau, Passau, Germany;3Department of Computer Engineering, Imam Reza (AS) International University, Mashhad, Iran
Keyword(s):Kolmogorov-Arnold Networks, Particle Swarm Optimization, Multi-Agent, Trajectory Forecasting, Intelligent Transportation System, Unmanned Aerial Vehicle, Feature Extraction.
Abstract:Accurate trajectory prediction for moving agents such as pedestrians and vehicles is essential for autonomous driving, intelligent navigation, and abnormal behavior detection. Real-time prediction of future movements enhances the development of autonomous vehicles and the efficiency of traffic management systems. In this study, a novel trajectory prediction approach based on Temporal Kolmogorov-Arnold Networks (TKAN) is introduced, using the TUMDOT-MUC dataset collected by Unmanned Aerial Vehicles (UAVs) in Munich, Germany, to model large-scale urban scenarios. To improve prediction accuracy, additional features were extracted from the primary dataset and incorporated into the TKAN architecture, demonstrating a marked performance improvement over general machine learning models. The accuracy of predictions is further refined by tuning hyperparameters of TKAN through Particle Swarm Optimization (PSO). The proposed model provides a robust and reliable solution for the trajectory prediction of multi-agents in challenging urban traffic conditions. This research advances intelligent and effective transportation systems by proposing scalable methods for improved traffic management and safety in densely populated urban areas, ultimately contributing to smarter and more efficient transportation networks.(More)
Accurate trajectory prediction for moving agents such as pedestrians and vehicles is essential for autonomous driving, intelligent navigation, and abnormal behavior detection. Real-time prediction of future movements enhances the development of autonomous vehicles and the efficiency of traffic management systems. In this study, a novel trajectory prediction approach based on Temporal Kolmogorov-Arnold Networks (TKAN) is introduced, using the TUMDOT-MUC dataset collected by Unmanned Aerial Vehicles (UAVs) in Munich, Germany, to model large-scale urban scenarios. To improve prediction accuracy, additional features were extracted from the primary dataset and incorporated into the TKAN architecture, demonstrating a marked performance improvement over general machine learning models. The accuracy of predictions is further refined by tuning hyperparameters of TKAN through Particle Swarm Optimization (PSO). The proposed model provides a robust and reliable solution for the trajectory prediction of multi-agents in challenging urban traffic conditions. This research advances intelligent and effective transportation systems by proposing scalable methods for improved traffic management and safety in densely populated urban areas, ultimately contributing to smarter and more efficient transportation networks.